From CRYO-EM Densities to Atom Coordinates and Ensembles with Bayes Approach

Abstract

Near-atom resolution in 3D cryo-electron microscopy brings two new challenges for refining atom coordinates to densities. Because their underlying potentials become more rugged with increased resolution, established refinement methods are easily trapped in local minima. Second, single structures no longer well describe the inherent structural diversity that can be resolved with cryo-EM.Here, we developed a method to derive cryo-EM refinement potentials from a Bayesian approach. Our refinement potential takes a physical model of the cryo-EM measuring and reconstruction process into account using Bayesian statistics. The result is a potential that statistically correctly reflects the given EM-data and is smooth even at high resolution. Previously developed algorithms are contained as limiting cases.With our method we are able to represent the configurational dynamics that is captured in cryo-EM density maps through a series of features. The smoothness of our refinement energy landscape allows efficient sampling and refinement while also taking into account thermal fluctuations. We provide an refinement force constant and potential from the Bayes approach that truthfully represents the complete distribution of atom configurations underlying the given EM maps. Thus, in combination with traditional molecular dynamics simulation we create refined ensembles that - as a whole - represent a given cryo-EM map.We further use the advantage of the Bayes approach to generate molecular dynamics ensembles that represent the simultaneous input from multiple cryo-EM maps together to capture the physically relevant transitions between different states as resolved by cryo-EM. Overall, our methods enables complete use of the data in cryo-EM maps and provides structural interpretation from ensembles that will aid understanding ever more complex cryo-EM data.